Associating a graphical element to media content item collections

ABSTRACT

Various embodiments provide for associating a collection of media items with a graphical element. For instance, a system can: generate corpus data from a set of features of a collection of media content items; determine a set of candidate graphical elements for the collection of media content items based on the corpus data and further based on a set of first mappings associating at least one graphical element and at least one n-gram; determine a set of prediction scores corresponding to the set of candidate graphical elements based on the set of features; determine a ranking for the set of candidate graphical elements based on the set of prediction stores; select a set of predicted graphical elements, from the set of candidate graphical elements, based on the ranking; and provide the set of predicted graphical elements in association with the collection of media content items.

TECHNICAL FIELD

Embodiments described herein relate to media content and, moreparticularly, but not by way of limitation, to systems, methods,devices, and instructions for associating a collection of media contentitems with a graphical element.

BACKGROUND

Mobile devices, such as smartphones, are often used to generate mediacontent items that can include, without limitation, text messages (e.g.,that include emojis or emoticons), digital images (e.g., photographs),videos, and animations. Messages can be organized into a collection(e.g., gallery) of messages, which an individual can share with otherindividuals over a network, such as through a social network.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate some embodimentsof the present disclosure and should not be considered as limiting itsscope. The drawings are not necessarily drawn to scale. To easilyidentify the discussion of any particular element or act, the mostsignificant digit or digits in a reference number refer to the figurenumber in which that element is first introduced, and like numerals maydescribe similar components in different views.

FIG. 1 is a block diagram showing an example messaging system, forexchanging data (e.g., messages and associated content) over a network,that can include a graphical element-to-collection association systemaccording to some embodiments.

FIG. 2 is block diagram illustrating further details regarding amessaging system that includes a graphical element-to-collectionassociation system, according to some embodiments.

FIG. 3 is a schematic diagram illustrating data which may be stored inthe database of the messaging system, according to some embodiments.

FIG. 4 is a schematic diagram illustrating a structure of a message,according to some embodiments, generated by a messaging clientapplication for communication.

FIG. 5 is a schematic diagram illustrating an example access-limitingprocess, in terms of which access to media content item (e.g., anephemeral message and associated multimedia payload of data) or a mediacontent item collection (e.g., an ephemeral message story) may betime-limited (e.g., made ephemeral).

FIG. 6 is a block diagram illustrating various modules of a graphicalelement-to-collection association system, according to some embodiments.

FIG. 7 illustrates examples graphical element mappings, according tosome embodiments.

FIGS. 8 and 9 are flowcharts illustrating methods for associating acollection of media items with a graphical element, according to certainembodiments.

FIG. 10 is a block diagram illustrating a representative softwarearchitecture, which may be used in conjunction with various hardwarearchitectures herein described.

FIG. 11 is a block diagram illustrating components of a machine,according to some embodiments, able to read instructions from amachine-readable medium (e.g., a machine-readable storage medium) andperform any one or more of the methodologies discussed herein.

DETAILED DESCRIPTION

Various embodiments provide systems, methods, devices, and instructionsfor automatically associating a collection of media items with agraphical element, such as an emoji or an emoticon. In particular, someembodiments predict (or infer) one or more graphical elements forassociation with a collection of media content items based on features(or signals) of the collection (e.g., associated captions, visuallabels, geographical location, category, event, graphical elementsetc.). One or more features of a collection may be those featuresautomatically determined for the collection during the collection'sautomatic generation (e.g., based on various factors or concepts, suchas topics, events, places, celebrities, space/time proximity, mediasources, or breaking news) or may be those features determined by anannotation process performed on the collection after its creation (e.g.,where the collection is user created). The predicted graphical elementscan be associated with the collection and, subsequently, provided inconnection with the collection or used to facilitate a search forcollections based on graphical elements. For instance, one or more ofthe predicted graphical elements may be presented to a user inconnection with the collection of the media content items, (e.g.,through a graphical user interface (GUI) accessible to the user at aclient device). Various embodiments described herein can overcome thechallenges of determining the relevance of graphical elements tocollections of media items when graphical elements are automaticallyassociated with the collections; determining the relevance of graphicalelements to collections of media items can be difficult given thatcertain graphical elements (e.g., emojis) may have connections with acollection that are based on more than what those certain graphicalelements depict or explicitly represent. Some such embodiments canenable or improve a computing device's ability to predict an associationbetween a collection of media items with a graphical element.

According to some embodiments, a system generates corpus data from a setof features of a collection of media content items. The systemdetermines a set of candidate graphical elements for the collection ofmedia content items based on the corpus data and further based on a setof first mappings associating at least one graphical element and atleast one n-gram (e.g., term-to-emoji or term-to-emoticon mapping). Thesystem determines a set of prediction (or inference) scorescorresponding to the set of candidate graphical elements based on theset of features. The system determines a ranking for the set ofcandidate graphical elements based on the set of prediction stores. Thesystem selects a set of predicted (or inferred) graphical elements, fromthe set of candidate graphical elements, based on the ranking.Eventually, the system provides the set of predicted graphical elementsin association with the collection of media content items. Alternativelyor additionally, the system stores an association between the set ofpredicted graphical elements and the collection of media content items,which can facilitate future retrieval or use during other operations,such as searching for collections based on one or more graphicalelements (e.g., provided in a search query by a user at a clientdevice).

As used herein, a graphical element can include, without limitation, anemoji, an emoticon, an icon, or the like. An emoticon may comprise apictograph, which may be defined as an extension to a text character setused in a writing system. For instance, an emoticon may be a pictographas defined by the Unicode standard or the Universal Coded Character Set(UCS). Example emoticons are illustrated with respect to FIG. 7. Anemoticon may comprise a typographic display of a facial representationusing text characters (e.g., “:-)” or “;-)”). Various embodiments aredescribed with respect to emojis for illustrative purposes and shouldnot be construed as limiting use of other types of graphical elements bysome embodiments.

As also used herein, an n-gram may include a unigram, a bigram, or atrigram. Example n-grams may include, without limitation, a word (e.g.,keyword), a term, or a phrase.

The description that follows includes systems, methods, techniques,instruction sequences, and computing machine program products thatembody illustrative embodiments of the disclosure. In the followingdescription, for the purposes of explanation, numerous specific detailsare set forth in order to provide an understanding of variousembodiments of the inventive subject matter. It will be evident,however, to those skilled in the art, that embodiments of the inventivesubject matter may be practiced without these specific details. Ingeneral, well-known instruction instances, protocols, structures, andtechniques are not necessarily shown in detail.

Reference will now be made in detail to embodiments of the presentdisclosure, examples of which are illustrated in the appended drawings.The present disclosure may, however, be embodied in many different formsand should not be construed as being limited to the embodiments setforth herein.

FIG. 1 is a block diagram showing an example messaging system 100, forexchanging data (e.g., messages and associated content) over a network106, that can include a graphical element-to-collection associationsystem, according to some embodiments. The messaging system 100 includesmultiple client devices 102, each of which hosts a number ofapplications including a messaging client application 104. Eachmessaging client application 104 is communicatively coupled to otherinstances of the messaging client application 104 and a messaging serversystem 108 via a network 106 (e.g., the Internet).

Accordingly, each messaging client application 104 can communicate andexchange data with another messaging client application 104 and with themessaging server system 108 via the network 106. The data exchangedbetween messaging client applications 104, and between a messagingclient application 104 and the messaging server system 108, includesfunctions (e.g., commands to invoke functions) as well as payload data(e.g., text, audio, video or other multimedia data).

The messaging server system 108 provides server-side functionality viathe network 106 to a particular messaging client application 104. Whilecertain functions of the messaging system 100 are described herein asbeing performed by either a messaging client application 104 or by themessaging server system 108, it will be appreciated that the location ofcertain functionality either within the messaging client application 104or the messaging server system 108 is a design choice. For example, itmay be technically preferable to initially deploy certain technology andfunctionality within the messaging server system 108, but to latermigrate this technology and functionality to the messaging clientapplication 104 where a client device 102 has a sufficient processingcapacity.

The messaging server system 108 supports various services and operationsthat are provided to the messaging client application 104. Suchoperations include transmitting data to, receiving data from, andprocessing data generated by the messaging client application 104. Thisdata may include message content, client device information, geolocationinformation, media annotation and overlays, message content persistenceconditions, social network information, and live event information asexamples. Data exchanges within the messaging system 100 are invoked andcontrolled through functions available via user interfaces (UIs) of themessaging client application 104.

Turning now specifically to the messaging server system 108, anApplication Program Interface (API) server 110 is coupled to, andprovides a programmatic interface to, an application server 112. Theapplication server 112 is communicatively coupled to a database server118, which facilitates access to a database 120 in which is stored dataassociated with messages processed by the application server 112.

Dealing specifically with the Application Program Interface (API) server110, this server receives and transmits message data (e.g., commands andmessage payloads) between the client device 102 and the applicationserver 112. Specifically, the API server 110 provides a set ofinterfaces (e.g., routines and protocols) that can be called or queriedby the messaging client application 104 in order to invoke functionalityof the application server 112. The API server 110 exposes variousfunctions supported by the application server 112, including accountregistration; login functionality; the sending of messages, via theapplication server 112, from a particular messaging client application104 to another messaging client application 104; the sending of mediafiles (e.g., digital images or video) from a messaging clientapplication 104 to the messaging server application 114, and forpossible access by another messaging client application 104; the settingof a collection of media content items (e.g., story), the retrieval of alist of friends of a user of a client device 102; the retrieval of suchcollections; the retrieval of messages and content, the adding anddeletion of friends to a social graph; the location of friends within asocial graph; and opening an application event (e.g., relating to themessaging client application 104).

The application server 112 hosts a number of applications, systems, andsubsystems, including a messaging server application 114, an annotationsystem 116, and a social network system 122. The messaging serverapplication 114 implements a number of message processing technologiesand functions, particularly related to the aggregation and otherprocessing of media content items (e.g., textual and multimedia contentitems) included in messages received from multiple instances of themessaging client application 104. As will be described herein, mediacontent items from multiple sources may be aggregated into collectionsof media content items (e.g., stories or galleries), which may beautomatically annotated by various embodiments described herein. Forexample, the collections of media content items can be annotated byassociating the collections with captions, geographic locations,categories, events, highlight media content items, and the like. Thecollections of media content items can be made available for access, bythe messaging server application 114, to the messaging clientapplication 104. Other processor- and memory-intensive processing ofdata may also be performed server-side by the messaging serverapplication 114, in view of the hardware requirements for suchprocessing.

For a given a collection of media content, one or more annotations ofthe given collection may represent features of the given collection, andthose features may include one or more graphical elements (e.g., emojisor emoticons) that various embodiments described herein may be use whenautomatically associating one or more graphical elements with the givencollection. Access to the given collection of media content items mayinclude access to one or more of annotations of the given collection andone or more graphical elements associated with the given collection byvarious embodiments described herein.

The application server 112 also includes an annotation system 116 thatis dedicated to performing various image processing operations,typically with respect to digital images or video received within thepayload of a message at the messaging server application 114.

The social network system 122 supports various social networkingfunctions and services, and makes these functions and services availableto the messaging server application 114. To this end, the social networksystem 122 maintains and accesses an entity graph 304 (FIG. 3) withinthe database 120. Examples of functions and services supported by thesocial network system 122 include the identification of other users ofthe messaging system 100 with which a particular user has relationshipsor is “following”, and also the identification of other entities andinterests of a particular user.

The application server 112 is communicatively coupled to a databaseserver 118, which facilitates access to a database 120 in which isstored data associated with messages (e.g., collections of messages)processed by the messaging server application 114.

FIG. 2 is block diagram illustrating further details regarding themessaging system 100 that includes a graphical element-to-collectionassociation system 208, according to some embodiments. Specifically, themessaging system 100 is shown to comprise the messaging clientapplication 104 and the application server 112, which in turn embody anumber of some subsystems, namely an ephemeral timer system 202, acollection management system 204, an annotation system 206, thegraphical element-to-collection association system 208.

The ephemeral timer system 202 is responsible for enforcing thetemporary access to content permitted by the messaging clientapplication 104 and the messaging server application 114. To this end,the ephemeral timer system 202 incorporates a number of timers that,based on duration and display parameters associated with a message, orcollection of messages (e.g., a story), selectively display and enableaccess to messages and associated content via the messaging clientapplication 104. Further details regarding the operation of theephemeral timer system 202 are provided below.

The collection management system 204 is responsible for managingcollections of media content items (e.g., collections of text, image,video, and audio data), which may be initially user curated orautomatically generated based on various factors or concepts (e.g.,topics, events, places, celebrities, space/time proximity, mediasources, breaking news, etc.) and then annotated as described herein. Insome examples, a collection of media content items (e.g., messages,including digital images, video, text, and audio) may be organized intoa “gallery,” such as an “event gallery” or an “event story.” Such acollection may be made available for a specified time period, such asthe duration of an event to which the media content items relate. Forexample, media content items relating to a music concert may be madeavailable as a “story” for the duration of that music concert. Thecollection management system 204 may also be responsible for publishingan icon that provides notification of the existence of a particularcollection to the user interface of the messaging client application104. According to some embodiments, the icon comprises one or more mediacontent items from the collection that are identified as highlight mediacontent items for the collection as described herein.

The collection management system 204 furthermore includes a curationinterface 210 that allows a collection manager to manage and curate aparticular collection of media content items. For example, the curationinterface 210 enables an event organizer to curate a collection of mediacontent items relating to a specific event (e.g., delete inappropriatemedia content items or redundant messages). Additionally, the collectionmanagement system 204 employs machine vision (or image recognitiontechnology) and media content item rules to automatically curate a mediacontent item collection. In certain embodiments, compensation may bepaid to a user for inclusion of user-generated media content items intoa collection. In such cases, the curation interface 210 operates toautomatically make payments to such users for the use of their mediacontent items.

The annotation system 206 determines and associates one or moreannotations for a collection of media content items. Annotations thatmay be determined for the collection of media content items can include,without limitation, a caption, a geographic location, a category, anevent, and a highlight media content item (e.g., for representing thecollection). According to various embodiments, one or more of theannotations determined by the annotation system 206 are used by thegraphical element-to-collection association system 208 as features of agiven collection of media content items that the graphicalelement-to-collection association system 208 uses for determining andassociating one or more graphical elements with the given collection.The annotation system 206 may determine a particular caption for aplurality of media content items by selecting the particular captionfrom a set of captions, where the set of captions being extracted fromthe plurality of media content items. The annotation system 206 maydetermine a particular geographic location for the plurality of mediacontent items. The annotation system 206 may determine a particularcategory for the plurality of media content items based on at least oneof analysis of a set of visual labels identified for the plurality ofmedia content items or analysis of at least one caption in the set ofcaptions. The annotation system 206 may generate a collection of mediacontent items that comprises the plurality of media content items andcollection annotation data that at least associates the collection withthe particular caption, with the particular geographic location, andwith the particular category. Alternatively or additionally, theannotation system 206 may associate user-specified annotations with acollection (e.g., annotations a user provides for, or applies to acollection, through a client device 102). Eventually, when a collectionof media content items is provided for access by a client deviceassociated with a user, annotations determined for the collection mayalso be provided with the collection. For instance, one or more of thepredicted graphical elements may be presented to a user in connectionwith the collection of the media content items through a graphical userinterface (GUI) accessible to the user at a client device.

The graphical element-to-collection association system 208 automaticallyassociates one or more graphical elements with a collection of mediacontent items. The graphical element-to-collection association system208 may do so by predicting the one or more graphical elements for thecollection of media content items based on features of the collection(e.g., associated captions, visual labels, geographical location,category, event, etc.). One or more features of a collection may bethose features automatically determined for the collection during thecollection's automatic generation (e.g., based on various factors orconcepts, such as topics, events, places, celebrities, space/timeproximity, media sources, or breaking news) or may be those featuresdetermined by an annotation process performed on the collection afterits creation (e.g., where the collection is user created).

According to some embodiments, the graphical element-to-collectionassociation system 208 associates one or more graphical elements with acollection of media content items by generating corpus data from a setof features of a collection of media content items. The graphicalelement-to-collection association system 208 then determines a set ofcandidate graphical elements for the collection of media content itemsbased on the corpus data and further based on at least one graphicalelement mapping. Example graphical element mappings can include, withoutlimitation, mappings between a graphical element (e.g., emoji) and ann-gram (e.g., word, term or phrase), mappings between two or moredifferent graphical elements (e.g., mapping an emoji to one or moresynonym emoji), and canonical mappings between a graphical element and acollection of media content items (e.g., based on a category associatedwith the collection). A graphical element mapping used by the graphicalelement-to-collection association system 208 may be one generated by thegraphical element-to-collection association system 208 or anothersystem. Once generated, a graphical element mapping may be stored in adata structure, such as a database table. The generation of one or moregraphical element mappings may be performed as part of a pre-processingstage of the graphical element-to-collection association system 208,where association of a new collection to one or more predicted graphicalelements is performed as part of a processing stage of the graphicalelement-to-collection association system 208.

Generation of graphical element mappings may be based on mining variousdata sources. For instance, a mapping between a graphical element and an-gram may be generated by analyzing co-occurrences of graphicalelements with n-grams in other collections (e.g., determining whichemojis commonly co-occur with terms across prior collections by anormalized pointwise mutual information (NPMI) algorithm), or by miningfor mapping between a graphical element and an n-gram by analyzingstandard descriptions of graphical elements provided by a character setstandard (e.g., Unicode standard description of emojis). A mappingbetween two or more graphical elements (e.g., two or more emojis) may begenerated by analyzing mappings between graphical elements and n-gramsto identify synonym graphical elements (e.g., synonym emojis). Acanonical mapping between a graphical element and a collection categorymay be manually established (e.g., by one or more human individuals)based on analysis or observation of prior collections (e.g., observingthat a particular graphical element represents a particular collectioncategory well).

Subsequently, the graphical element-to-collection association system 208then determines a set of prediction scores corresponding to the set ofcandidate graphical elements based on the set of features. The graphicalelement-to-collection association system 208 determines a ranking forthe set of candidate graphical elements based on the set of predictionstores. The graphical element-to-collection association system 208selects a set of predicted graphical elements, from the set of candidategraphical elements, based on the ranking.

The graphical element-to-collection association system 208 can thenprovide the set of predicted graphical elements in association with thecollection of media content items. Alternatively or additionally, thegraphical element-to-collection association system 208 may store anassociation between the collection and the set of predicted graphicalelements (e.g., store the association in the annotation table 312 or thestory table 306 in connection with a collection of messages). The storedassociation can be subsequently retrieved and used for other operations,such as searching for collections based on one or more graphicalelements (e.g., provided in a search query by a user at a client device102).

For some embodiments, graphical element predictions (or inferences) bythe graphical element-to-collection association system 208 are evaluatedand the graphical element-to-collection association system 208 tunedbased on the evaluation. In this way, the graphicalelement-to-collection association system 208 can maximize a graphicalelement (e.g., emoji) to collection (e.g., story) relevance metric totune the graphical element-to-collection association system 208. Forinstance, the graphical element-to-collection association system 208 maybe tuned via a manual evaluation by a human individual (e.g., trainedhuman curator) at a client device. The human individual may be presentedwith a given collection of media content items as a sequence of captionsand with one or more graphical elements (e.g., emojis) extracted fromthe collection, which the human individual can then review. The humanindividual may mark or otherwise designate whether a particularextracted graphical element is relevant to the given collection (e.g.,on a three-point scale of not relevant, relevant, or highly relevant).Based on this relevancy evaluation, the graphical element-to-collectionassociation system 208 can modify the weights (e.g., representing anemoji-to-collection relevance) used by the graphicalelement-to-collection association system 208 when determining one ormore prediction scores for candidate graphical elements. This, in turn,can improve the graphical element-to-collection association system 208'sability (e.g., precision and recall of relevant graphical elementassociations) to predict graphical elements in the future.

FIG. 3 is a schematic diagram illustrating data 300 which may be storedin the database 120 of the messaging server system 108, according tocertain embodiments. While the content of the database 120 is shown tocomprise a number of tables, it will be appreciated that the data couldbe stored in other types of data structures (e.g., as an object-orienteddatabase).

The database 120 includes message data stored within a message table314. The entity table 302 stores entity data, including an entity graph304. Entities for which records are maintained within the entity table302 may include individuals, corporate entities, organizations, objects,places, events etc. Regardless of type, any entity regarding which themessaging server system 108 stores data may be a recognized entity. Eachentity is provided with a unique identifier, as well as an entity typeidentifier (not shown).

The entity graph 304 furthermore stores information regardingrelationships and associations between entities. Such relationships maybe social, professional (e.g., work at a common corporation ororganization) interest-based or activity-based, merely for example.

In an annotation table 312, the database 120 also stores annotationdata, such as annotations applied to a message or a collection of mediacontent items. As described herein, annotations applied to a collectionof media content items can include, without limitation, a caption (e.g.,single word or phrase), a geographic location, a category, an event(e.g., periodic event, ongoing event, or concluded event), and ahighlight media content item (e.g., for representing the collection).Annotations applied to a message may include, for example, filters,media overlays, texture fills and sample digital images. Filters, mediaoverlays, texture fills, and sample digital images for which data isstored within the annotation table 312 are associated with and appliedto videos (for which data is stored in a video table 310) or digitalimages (for which data is stored in an image table 308). In one example,an image overlay can be displayed as overlaid on a digital image orvideo during presentation to a recipient user. For example, a user mayappend a media overlay on a selected portion of the digital image,resulting in presentation of an annotated digital image that includesthe media overlay over the selected portion of the digital image. Inthis way, a media overlay can be used, for example, as a digital stickeror a texture fill that a user can use to annotate or otherwise enhance adigital image, which may be captured by a user (e.g., photograph).

Filters may be of various types, including user-selected filters from agallery of filters presented to a sending user by the messaging clientapplication 104 when the sending user is composing a message. Othertypes of filters include geolocation filters (also known as geo-filters)which may be presented to a sending user based on geographic location.For example, geolocation filters specific to a neighborhood or speciallocation may be presented within a user interface by the messagingclient application 104, based on geolocation information determined by aGPS unit of the client device 102. Another type of filter is a datafilter, which may be selectively presented to a sending user by themessaging client application 104, based on other inputs or informationgathered by the client device 102 during the message creation process.Examples of data filters include current temperature at a specificlocation, a current speed at which a sending user is traveling, batterylife for a client device 102, or the current time.

Other annotation data that may be stored within the image table 308 isso-called “lens” data. A “lens” may be a real-time special effect andsound that may be added to an image or a video.

As mentioned above, the video table 310 stores video data which, in oneembodiment, is associated with messages for which records are maintainedwithin the message table 314. Similarly, the image table 308 storesimage data associated with messages for which message data is stored inthe entity table 302. The entity table 302 may associate variousannotations from the annotation table 312 with various images and videosstored in the image table 308 and the video table 310.

A story table 306 stores data regarding collections of messages andassociated image, video, or audio data, which are compiled into acollection of media content items (e.g., a story or a gallery). Thecreation of a particular collection may be initiated by a particularuser (e.g., each user for which a record is maintained in the entitytable 302) or automatically generated based on various factors orconcepts (e.g., topics, events, places, celebrities, space/timeproximity, media sources, breaking news, etc.). A user may create a“personal story” in the form of a collection of media content items thathas been created and sent/broadcast by that user. To this end, the userinterface of the messaging client application 104 may include an iconthat is user-selectable to enable a sending user to add a specific mediacontent item to his or her personal story.

A collection may also constitute a “live story,” which is a collectionof media content items from multiple users that is created manually,automatically, or using a combination of manual and automatictechniques. For example, a “live story” may constitute a curated streamof user-submitted media content items from various locations and events.Users whose client devices have location services enabled and are at acommon location or event at a particular time may, for example, bepresented with an option, via a user interface of the messaging clientapplication 104, to contribute media content items to a particular livestory. The live story may be identified to the user by the messagingclient application 104, based on his or her location. The end result isa “live story” told from a community perspective.

A further type of media content item collection is known as a “locationstory,” which enables a user whose client device 102 is located within aspecific geographic location (e.g., on a college or university campus)to contribute to a particular collection. In some embodiments, acontribution to a location story may require a second degree ofauthentication to verify that the end user belongs to a specificorganization or other entity (e.g., is a student on the universitycampus).

FIG. 4 is a schematic diagram illustrating a structure of a message 400,according to some in some embodiments, generated by a messaging clientapplication 104 for communication to a further messaging clientapplication 104 or the messaging server application 114. The content ofa particular message 400 is used to populate the message table 314stored within the database 120, accessible by the messaging serverapplication 114. Similarly, the content of a message 400 is stored inmemory as “in-transit” or “in-flight” data of the client device 102 orthe application server 112. The message 400 is shown to include thefollowing components:

-   -   A message identifier 402: a unique identifier that identifies        the message 400.    -   A message text payload 404: text, to be generated by a user via        a user interface of the client device 102 and that is included        in the message 400.    -   A message image payload 406: image data, captured by a camera        component of a client device 102 or retrieved from memory of a        client device 102, and that is included in the message 400.    -   A message video payload 408: video data, captured by a camera        component or retrieved from a memory component of the client        device 102 and that is included in the message 400.    -   A message audio payload 410: audio data, captured by a        microphone or retrieved from the memory component of the client        device 102, and that is included in the message 400.    -   A message annotation 412: annotation data (e.g., filters,        stickers, texture fills, or other enhancements) that represents        annotations to be applied to message image payload 406, message        video payload 408, or message audio payload 410 of the message        400.    -   A message duration parameter 414: a parameter value indicating,        in seconds, the amount of time for which content of the message        400 (e.g., the message image payload 406, message video payload        408, message audio payload 410) is to be presented or made        accessible to a user via the messaging client application 104.    -   A message geolocation parameter 416: geolocation data (e.g.,        latitudinal and longitudinal coordinates) associated with the        content payload of the message 400. Multiple message geolocation        parameter 416 values may be included in the payload, each of        these parameter values being associated with respect to media        content items included in the content (e.g., a specific image        within the message image payload 406, or a specific video in the        message video payload 408).    -   A message story identifier 418: identifier values identifying        one or more media content item collections (e.g., “stories”)        with which a particular media content item in the message image        payload 406 of the message 400 is associated. For example,        multiple images within the message image payload 406 may each be        associated with multiple media content item collections using        identifier values.    -   A message tag 420: each message 400 may be tagged with multiple        tags, each of which is indicative of the subject matter of        content included in the message payload. For example, where a        particular image included in the message image payload 406        depicts an animal (e.g., a lion), a tag value may be included        within the message tag 420 that is indicative of the relevant        animal. Tag values may be generated manually, based on user        input, or may be automatically generated using, for example,        image recognition.    -   A message sender identifier 422: an identifier (e.g., a        messaging system identifier, email address or device identifier)        indicative of a user of the client device 102 on which the        message 400 was generated and from which the message 400 was        sent.    -   A message receiver identifier 424: an identifier (e.g., a        messaging system identifier, email address, or device        identifier) indicative of a user of the client device 102 to        which the message 400 is addressed.

The contents (e.g., values) of the various components of message 400 maybe pointers to locations in tables within which content data values arestored. For example, an image value in the message image payload 406 maybe a pointer to (or address of) a location within an image table 308.Similarly, values within the message video payload 408 may point to datastored within a video table 310, values stored within the messageannotations 412 may point to data stored in an annotation table 312,values stored within the message story identifier 418 may point to datastored in a story table 306, and values stored within the message senderidentifier 422 and the message receiver identifier 424 may point to userrecords stored within an entity table 302.

FIG. 5 is a schematic diagram illustrating an access-limiting process500, in terms of which access to a media content item (e.g., anephemeral message 502, and associated multimedia payload of data) or amedia content item collection (e.g., an ephemeral message story 504) maybe time-limited (e.g., made ephemeral). Though the access-limitingprocess 500 is described below with respect to the ephemeral message 502and the ephemeral message story 504, for the access-limiting process 500can be applied to another type of media content item or collection ofmedia content items, such as a collection of media content itemsassociated with one or more graphical elements by an embodimentdescribed herein.

An ephemeral message 502 is shown to be associated with a messageduration parameter 506, the value of which determines an amount of timethat the ephemeral message 502 will be displayed to a receiving user ofthe ephemeral message 502 by the messaging client application 104. Inone embodiment, an ephemeral message 502 is viewable by a receiving userfor up to a maximum of 10 seconds, depending on the amount of time thatthe sending user specifies using the message duration parameter 506.

The message duration parameter 506 and the message receiver identifier424 are shown to be inputs to a message timer 512, which is responsiblefor determining the amount of time that the ephemeral message 502 isshown to a particular receiving user identified by the message receiveridentifier 424. In particular, the ephemeral message 502 will only beshown to the relevant receiving user for a time period determined by thevalue of the message duration parameter 506. The message timer 512 isshown to provide output to a more generalized ephemeral timer system202, which is responsible for the overall timing of display of content(e.g., an ephemeral message 502) to a receiving user.

The ephemeral message 502 is shown in FIG. 5 to be included within anephemeral message story 504 (e.g., a personal story, or an event story).The ephemeral message story 504 has an associated story durationparameter 508, a value of which determines a time duration for which theephemeral message story 504 is presented and accessible to users of themessaging system 100. The story duration parameter 508, for example, maybe the duration of a music concert, where the ephemeral message story504 is a collection of media content items pertaining to that concert.Alternatively, a user (either the owning user or a curator user) mayspecify the value for the story duration parameter 508 when performingthe setup and creation of the ephemeral message story 504.

Additionally, each ephemeral message 502 within the ephemeral messagestory 504 has an associated story participation parameter 510, a valueof which determines the duration of time for which the ephemeral message502 will be accessible within the context of the ephemeral message story504. Accordingly, a particular ephemeral message story 504 may “expire”and become inaccessible within the context of the ephemeral messagestory 504, prior to the ephemeral message story 504 itself expiring interms of the story duration parameter 508. The story duration parameter508, story participation parameter 510, and message receiver identifier424 each provide input to a story timer 514, which operationallydetermines, firstly, whether a particular ephemeral message 502 of theephemeral message story 504 will be displayed to a particular receivinguser and, if so, for how long. Note that the ephemeral message story 504is also aware of the identity of the particular receiving user as aresult of the message receiver identifier 424.

Accordingly, the story timer 514 operationally controls the overalllifespan of an associated ephemeral message story 504, as well as anindividual ephemeral message 502 included in the ephemeral message story504. In one embodiment, each and every ephemeral message 502 within theephemeral message story 504 remains viewable and accessible for a timeperiod specified by the story duration parameter 508. In a furtherembodiment, a certain ephemeral message 502 may expire, within thecontext of ephemeral message story 504, based on a story participationparameter 510. Note that a message duration parameter 506 may stilldetermine the duration of time for which a particular ephemeral message502 is displayed to a receiving user, even within the context of theephemeral message story 504. Accordingly, the message duration parameter506 determines the duration of time that a particular ephemeral message502 is displayed to a receiving user, regardless of whether thereceiving user is viewing that ephemeral message 502 inside or outsidethe context of an ephemeral message story 504.

The ephemeral timer system 202 may furthermore operationally remove aparticular ephemeral message 502 from the ephemeral message story 504based on a determination that it has exceeded an associated storyparticipation parameter 510. For example, when a sending user hasestablished a story participation parameter 510 of 24 hours fromposting, the ephemeral timer system 202 will remove the relevantephemeral message 502 from the ephemeral message story 504 after thespecified 24 hours. The ephemeral timer system 202 also operates toremove an ephemeral message story 504 either when the storyparticipation parameter 510 for each and every ephemeral message 502within the ephemeral message story 504 has expired, or when theephemeral message story 504 itself has expired in terms of the storyduration parameter 508.

In certain use cases, a creator of a particular ephemeral message story504 may specify an indefinite story duration parameter 508. In thiscase, the expiration of the story participation parameter 510 for thelast remaining ephemeral message 502 within the ephemeral message story504 will determine when the ephemeral message story 504 itself expires.In this case, a new ephemeral message 502, added to the ephemeralmessage story 504, with a new story participation parameter 510,effectively extends the life of an ephemeral message story 504 to equalthe value of the story participation parameter 510.

Responsive to the ephemeral timer system 202 determining that anephemeral message story 504 has expired (e.g., is no longer accessible),the ephemeral timer system 202 communicates with the messaging system100 (and, for example, specifically the messaging client application104) to cause an indicium (e.g., an icon) associated with the relevantephemeral message story 504 to no longer be displayed within a userinterface of the messaging client application 104. Similarly, when theephemeral timer system 202 determines that the message durationparameter 506 for a particular ephemeral message 502 has expired, theephemeral timer system 202 causes the messaging client application 104to no longer display an indicium (e.g., an icon or textualidentification) associated with the ephemeral message 502.

FIG. 6 is a block diagram illustrating various modules of the graphicalelement-to-collection association system 208, according to someembodiments. The graphical element-to-collection association system 208is shown as including a corpus generation module 602, a graphicalelement candidate determination module 604, a prediction scoredetermination module 606, a ranking determination module 608, agraphical element selection module 610, a graphical element providermodule 612, a tuning module 614, and a mapping generation module 616.The various modules of the graphical element-to-collection associationsystem 208 are configured to communicate with each other (e.g., via abus, shared memory, or a switch). Any one or more of these modules maybe implemented using one or more processors 600 (e.g., by configuringsuch one or more processors 600 to perform functions described for thatmodule) and hence may include one or more of the processors 600.

Any one or more of the modules described may be implemented usinghardware alone (e.g., one or more of the computer processors of amachine, such as machine 1100) or a combination of hardware andsoftware. For example, any described module of the graphicalelement-to-collection association system 208 may physically include anarrangement of one or more of the processors 600 (e.g., a subset of oramong the one or more processors of the machine, such the machine 1100)configured to perform the operations described herein for that module.As another example, any module of the graphical element-to-collectionassociation system 208 may include software, hardware, or both, thatconfigure an arrangement of one or more processors 600 (e.g., among theone or more processors of the machine, such as the machine 1100)) toperform the operations described herein for that module. Accordingly,different modules of the graphical element-to-collection associationsystem 208 may include and configure different arrangements of suchprocessors 600 or a single arrangement of such processors 600 atdifferent points in time. Moreover, any two or more modules of thegraphical element-to-collection association system 208 may be combinedinto a single module, and the functions described herein for a singlemodule may be subdivided among multiple modules. Furthermore, accordingto various embodiments, modules described herein as being implementedwithin a single machine, database, or device may be distributed acrossmultiple machines, databases, or devices.

The corpus generation module 602 generates corpus data from a set offeatures of a collection of media content items, the corpus datacomprising a set of n-grams identified in the set of features, a givenn-gram in the set of n-grams comprising at least one of a term or agraphical element. In particular, for some embodiments, the corpusgeneration module 602 identifies (e.g., all) terms, words, and graphicalelements found in features of the collection, such as a caption of thecollection, a graphical element (e.g., emoji) present in the collection,a visual label identifying an object depicted in a media content item ofthe collection, or a category associated with the collection (e.g.,sports event, concert, fashion show, animals story, etc.). The corpusgeneration module 602 may combine the identified terms, words, andgraphical elements into a single corpus, which can be described by thecorpus data. In particular, the corpus data generated by the corpusgeneration module 602 may describe each unique n-gram (e.g., unigram,bigram, trigram, etc.) found in the single corpus. Additionally, thecorpus generation module 602 can generate a frequency probability scorefor each unique n-gram found in the single corpus. The frequencyprobability score may be represented as a single count of times a uniquen-gram appears in the single corpus.

The graphical element candidate determination module 604 determines aset of candidate graphical elements for the collection of media contentitems based on the corpus data generated by the corpus generation module602, and further based on at least one graphical element mapping.Examples of graphic element mappings may include, without limitation: aset of mappings associating at least one graphical element and at leastone n-gram (e.g., emoji-to-word mapping); a set of mappings associatingtwo or more graphical elements together (e.g., emoji-to-emoji mappingsynonyms); and a set of between at least one graphical element and acategory associated with a given collection of media content items(e.g., canonical mapping of an emoji-to-collection category). Accordingto some embodiments, the mapping generation module 616 generates one ormore graphical element mappings used by the graphicalelement-to-collection association system 208.

For some embodiments, the graphical element candidate determinationmodule 604 determines the set of candidate graphical elements based onthe corpus data by adding any graphical elements described in the corpusdata (e.g., identified by the corpus generation module 602 in thefeatures of the collection) to the set of candidate graphical elements.

According some embodiments, the graphical element candidatedetermination module 604 determines the set of candidate graphicalelements based on the corpus data generated by the corpus generationmodule 602 and further based on a set of mappings associating at leastone graphical element and at least one n-gram (e.g., emoji-to-wordmapping). In particular, based on the set of mappings associating atleast one graphical element and at least one n-gram, the graphicalelement candidate determination module 604 may map at least one uniquen-gram (e.g., “happy birthday”) described in the corpus data to one ormore graphical elements (e.g., an emoji depicting a birthday cake), andadd those one or more graphical elements to the set of candidategraphical elements. Depending on the embodiment, mapping a unique n-gramto one or more graphical elements may comprise replacing at least a termportion of the unique n-gram (e.g., a word or phrase of the uniquen-gram) with the one or more graphical elements according to the set ofmappings, thereby producing a modified n-gram that can be added to thecandidate graphical elements.

For various embodiments, the graphical element candidate determinationmodule 604 determines, based on the corpus data generated by the corpusgeneration module 602 and further based on a set of mappings associatingtwo or more graphical elements together (e.g., emoji-to-emoji mappingsynonyms). For instance, based on the set of mappings associating two ormore graphical elements together, the graphical element candidatedetermination module 604 may map at least one graphical element (e.g.,an emoji depicting a birthday cake) described in the corpus data to oneor more graphical element representing synonyms (e.g., an emojidepicting confetti or an emoji depicting a birthday cupcake) to the atleast one graphical element, and add those one or more graphicalelements to the set of candidate graphical elements.

For some embodiments, the graphical element candidate determinationmodule 604 determines a graphical element based on the corpus datagenerated by the corpus generation module 602 and further based on a setof mappings between at least one graphical element and a categoryassociated with a given collection of media content items (e.g.,canonical mapping of an emoji-to-collection category). For example, thegraphical element candidate determination module 604 element candidatemay determine a category (e.g., “video games”) associated with thecollection (e.g., as identified by a feature of the collection), map thecollection to one or more graphical elements (e.g., an emoji depicting agame console controller) based on the set of mappings between at leastone graphical element and a category associated with a given collectionof media content items, and add those one or more graphical elements tothe set of candidate graphical elements.

The prediction score determination module 606 determines a set ofprediction scores corresponding to the set of candidate graphicalelements, determined by the graphical element candidate determinationmodule 604, based on the set of features of the collection of mediacontent items. According to some embodiments, the prediction scoredetermination module 606 determines a prediction score for eachgraphical element in the set of candidate graphical elements based oncorresponding frequency probability scores provided by the corpus data(generated by the corpus generation module 602). The prediction scoredetermination module 606 may generate each prediction score a termfrequency-inverse document frequency (TF-IDF) algorithm (e.g., aweighted TF-IDF). For instance, the prediction score determinationmodule 606 may use the following Equation 1:

${{Prediction}\mspace{14mu}{Score}{= {\sum\limits_{t \in s}\frac{{Ctf}*{TFt}}{{Cidf}*{IDFt}}}}},$where t represents a particular n-gram or graphical element in thecorpus, S represents the corpus of n-grams described by the corpus datagenerated by the corpus generation module 602, TF_(t) represents thefrequency probability score corresponding to t, IDF_(t) represents theIDF score for t across the corpus S, and each of Ctf and Cid_(f)represents a constant that can be tuned via the tuning module 614.

The ranking determination module 608 determines a ranking for the set ofcandidate graphical elements, determined by the graphical elementcandidate determination module 604, based on the set of predictionstores determined by the prediction score determination module 606. Inparticular, in the set of candidate graphical elements, a graphicalelement having a higher prediction score may be determined to have ahigher rank.

The graphical element selection module 610 selects a set of predictedgraphical elements, from the set of candidate graphical elements(determined by the graphical element candidate determination module604), based on the ranking determined by the ranking determinationmodule 608. In particular, the set of predicted graphical elementsselected by the graphical element selection module 610 may be thosehaving the highest rank as determined by the ranking determinationmodule 608.

The graphical element provider module 612 provides the set of predictedgraphical elements, selected by the graphical element selection module610, in association with the collection of media content items. Forexample, when a collection of media content items is provided for accessby a client device associated with a user, the set of predictedgraphical elements selected by the graphical element selection module610 for the collection may also be provided for access with thecollection. Alternatively or additionally, the graphical elementprovider module 612 may store an association between the collection andthe set of predicted graphical elements (e.g., store the association inthe annotation table 312 or the story table 306 in connection with acollection of messages). The stored association can be subsequentlyretrieved and used for other operations, such as searching forcollections based on one or more graphical elements (e.g., provided in asearch query by a user at a client device 102). Further, providing theset of predicted graphical elements in association with the collectionof media content items may comprise causing presentation of one or moreof the set of predicted graphical elements to a user in connection withthe collection of the media content items (e.g., through a graphicaluser interface (GUI) accessible to the user at a client device).

The tuning module 614 tunes how the prediction score determinationmodule 606 determines a set of prediction scores based on the set offeatures by adjusting at least one weight (e.g., representing anemoji-to-collection relevance) used by the prediction scoredetermination module 606 in calculating a prediction score for at leastone of the graphical elements in the set of candidate graphicalelements. According to some embodiments, the tuning module 614 adjustsat least one of constant Ct_(f) and constant Cid_(f) of Equation 1 asdescribed above with respect to the prediction score determinationmodule 606. Depending on the embodiment, the tuning module 614 may tunethe prediction score determination module 606 by facilitating a manualevaluation by a human individual (e.g., trained human curator) at aclient device. In particular, through the tuning module 614, the humanindividual may be presented with a given collection of media contentitems as a sequence of captions and with one or more graphical elements(e.g., emojis) extracted from the collection, which the human individualcan then review. By the tuning module 614, the human individual may markor otherwise designate whether a particular extracted graphical elementis relevant to the given collection (e.g., on a three-point scale of notrelevant, relevant, or highly relevant). Based on this relevancyevaluation, the tuning module 614 can modify the weights used by theprediction score determination module 606 to determine one or moreprediction scores for candidate graphical elements. In this way, thetuning module 614 may improve the graphical element-to-collectionassociation system 208's ability (e.g., precision and recall of relevantgraphical element associations) to predict graphical elements in thefuture.

The mapping generation module 616 generates one or more graphicalelements mappings that can be used by the graphicalelement-to-collection association system 208. As noted herein, examplesof graphic element mappings can include, without limitation, a set ofmappings associating at least one graphical element and at least onen-gram, a set of mappings associating two or more graphical elementstogether, and a set of between at least one graphical element and acategory associated with a given collection of media content items.

According to some embodiments, the mapping generation module 616generates (e.g., mines for) a set of mappings associating at least onegraphical element and at least one n-gram based on co-occurrences of theat least one graphical element and the at least one n-gram with respectto (e.g., within) at least one other collection of media content items,such as a corpus of past collections (e.g., past stories, also referredto as historical stories). For example, based on historically observinga graphical element (e.g., emoji) depicting a farmer co-occurring withthe term “organic” frequently, the mapping generation module 616 cangenerate a graphical element-to-n-gram mapping with a certain weightbetween “organic” and the graphical element depicting the farmer.

For some embodiments, the mapping generation module 616 determinesn-grams that commonly co-occur with graphical elements (e.g., emojis)across a corpus of past collections using a normalized pointwise mutualinformation (NPMI) algorithm. In particular, first the mappinggeneration module 616 may generate inverse document frequency (IDF)scores for each graphical element (e.g., emoji) and n-gram (e.g., term)in the corpus of past collections. These can measure how often graphicalelements and n-grams (e.g., terms) appear in the corpus of pastcollections as a whole, thereby permitting the mapping generation module616 to identify n-grams (e.g., terms) that are frequently used in pastcollections versus n-grams (e.g., terms) that are uniquely related tothe collection at hand. Each IDF score may be represented as a simpleprobability p(x). Subsequently, the mapping generation module 616 maydetermine a weight of the graphical element-to-n-gram mappings using anormalized pointwise mutual information (NPMI) algorithm, where p(x) isthe probability of a n-gram (e.g., term) x appearing in a corpus of pastcollections, where p(y) is the probability of a graphical element (e.g.,emoji) y appearing in a corpus of past collections, and p(x,y) is theprobability of n-gram x and graphical element y co-occuring in the samedocument (e.g., media content item) in the corpus. The mappinggeneration module 616 may determine a weight (e.g., NPMI score) usingthe following Equation 2 to determine the weight between [−1,1] on the“correlated”-ness of a n-gram x and a graphical element y:

${{{npmi}\left( {x;y} \right)} = \frac{pm{i\left( {x,y} \right)}}{- {\log\left( {p\left( {x,y} \right)} \right)}}},$where the Equation 3 defines pmi(x, y) as follows:

${pm{i\left( {x;y} \right)}} \equiv {\log{\frac{p\left( {x,y} \right)}{{p(x)}{p(y)}}.}}$

For some embodiments, weights (e.g., NPMI score) are stored in a largescale map data structure, which may permit keys to be rapidly accessedto terms and related graphical elements (e.g., emojis).

According to some embodiments, the mapping generation module 616generates (e.g., mines for) a set of mappings associating at least onegraphical element and at least one n-gram based on data that provides aUnicode standard description for the at least one graphical element. Forinstance, based on the Unicode standard description associated with theUnicode emoji depicting a male farmer (U+1F468 U+200D U+1F33E), themapping generation module 616 may associate the graphical element withthe terms (e.g., {farmer gardener man rancher}) extracted or matchedagainst from the Unicode standard description.

According to some embodiments, the mapping generation module 616generates (e.g., mines for) a set of mappings associating two or moregraphical elements together. In particular, the mapping generationmodule 616 may generate the set of mappings associating two or moregraphical elements together based a set of mappings associating the atleast one graphical element and the at least one n-gram (e.g., alsogenerated by the mapping generation module 616). A set of mappingsassociating two or more graphical elements together may, for example,map emojis to one or more synonymous emoji. For instance, with respectto graphical elements meant to represent a Halloween party (e.g., anemoji depicting a jack-o-lantern and another emoji depicting a partyfavor), the mapping generation module 616 can learn that ajack-o-lantern graphical element and a party favor graphical element arenot synonyms but, rather, combined together represent a similar term. Tolearn this graphical element-to-graphical element mapping, the mappinggeneration module 616 may map the jack-o-lantern graphical element andthe party favor graphical element back to their respective n-gramrepresentations (e.g., {halloween,halloween party,pumpkin,jacko'lantern} for the jack-o-lantern graphical element and{party,celebration,birthday party,halloween party} for the party favorgraphical element) using the set of mappings associating at least onegraphical element to at least one n-gram. The mapping generation module616 may then map the resulting n-grams back to the synonym emojis (e.g.,{halloween,halloween party,pumpkin,jack o'lantern} can map to can map tosynonym graphical elements representing a party favor, a ghost, and ademon; and {party,celebration,birthday party,halloween party} can map tosynonym graphical elements representing a party favor, a present, ajack-o-lantern, and a ghost) using the set of mappings associating atleast one graphical element to at least one n-gram. The set of mappingsassociating two or more graphical elements together may also be designedto preserve high-value modifiers for emojis (e.g. skin tone) by usingcompound terms in the mappings (e.g. “tone1-woman”).

According to some embodiments, the mapping generation module 616generates a set of between at least one graphical element and a categoryassociated with a given collection of media content items. Inparticular, the mapping generation module 616 may permit a user at aclient device to establish a canonical mapping between a graphicalelement and a collection category based on analysis or observation ofprior collections, such as observing that a particular graphical elementrepresents a particular collection category well.

FIG. 7 illustrates examples graphical element mappings, according tosome embodiments. In particular, mapping 702 represents an examplegraphical element-to-n-gram mapping, and mapping 704 represents anexample graphical element-to-graphical element mapping. One or more ofthe mappings 702, 704 may be generated by the mapping generation module616 or used by the graphical element candidate determination module 604.Table 706 represents an example of data generated and stored by thegraphical element-to-collection association system 208 during itsoperation as described herein. In particular, column emoji provides theemoji in question, column score provides the score calculated by theprediction score determination module 606 for the emoji in question,column tf provides a frequency probability score corresponding to theemoji in question, column idf provides an IDF score for the emoji,column caps provides a number of captions that include the emoji inquestion, column sim_terms provides one or more terms that relate to theemoji in question, and column rm_sim_emoji provides one or more emojissimilar (e.g., synonyms) to the emoji in question.

FIG. 8 is a flowchart illustrating a method 800 for associating acollection of media items with a graphical element, according to certainembodiments. The method 800 may be embodied in computer-readableinstructions for execution by one or more computer processors such thatthe operations of the method 800 may be performed in part or in whole bythe messaging server system 108 or, more specifically, the graphicalelement-to-collection association system 208 of the messaging serverapplication 114. Accordingly, the method 800 is described below by wayof example with reference to the graphical element-to-collectionassociation system 208. At least some of the operations of the method800 may be deployed on various other hardware configurations, and themethod 800 is not intended to be limited to being operated by themessaging server system 108. Though the steps of method 800 may bedepicted and described in a certain order, the order in which the stepsare performed may vary between embodiments. For example, a step may beperformed before, after, or concurrently with another step.Additionally, the components described above with respect to the method800 are merely examples of components that may be used with the method800, and other components may also be utilized, in some embodiments.

At operation 802, the corpus generation module 602 generates corpus datafrom a set of features of a collection of media content items, thecorpus data comprising a set of n-grams identified in the set offeatures, a given n-gram in the set of n-grams comprising at least oneof a term or a graphical element. At operation 804, the graphicalelement candidate determination module 604 determines a set of candidategraphical elements for the collection of media content items based onthe corpus data generated by the corpus generation module 602, andfurther based on at least one graphical elements mapping. At operation806, the prediction score determination module 606 determines a set ofprediction scores corresponding to the set of candidate graphicalelements, determined by the graphical element candidate determinationmodule 604, based on the set of features of the collection of mediacontent items. At operation 808, the ranking determination module 608determines a ranking for the set of candidate graphical elements,determined by the graphical element candidate determination module 604,based on the set of prediction stores determined by the prediction scoredetermination module 606. At operation 810, the graphical elementselection module 610 selects a set of predicted graphical elements, fromthe set of candidate graphical elements (determined by the graphicalelement candidate determination module 604), based on the rankingdetermined by the ranking determination module 608. At operation 812,the graphical element provider module 612 provides the set of predictedgraphical elements, selected by the graphical element selection module610, in association with the collection of media content items.

FIG. 9 is a flowchart illustrating a method 900 for associating acollection of media items with a graphical element, according to certainembodiments. The method 900 may be embodied in computer-readableinstructions for execution by one or more computer processors such thatthe operations of the method 900 may be performed in part or in whole bythe messaging server system 108 or, more specifically, the graphicalelement-to-collection association system 208 of the messaging serverapplication 114. Accordingly, the method 900 is described below by wayof example with reference to the graphical element-to-collectionassociation system 208. At least some of the operations of the method900 may be deployed on various other hardware configurations, and themethod 900 is not intended to be limited to being operated by themessaging server system 108. Though the steps of method 900 may bedepicted and described in a certain order, the order in which the stepsare performed may vary between embodiments. For example, a step may beperformed before, after, or concurrently with another step.Additionally, the components described above with respect to the method900 are merely examples of components that may be used with the method900, and that other components may also be utilized, in someembodiments.

At operation 902, the mapping generation module 616 generates one ormore graphical element mappings that can be used by the graphicalelement-to-collection association system 208. For some embodiments,operations 904 through 914 are respectively similar to operations 802through 812 of the method 800 described above with respect to FIG. 8. Atoperation 916, the tuning module 614 tunes how the prediction scoredetermination module 606 determines a set of prediction scores based onthe set of features by adjusting at least one weight (e.g., representingan emoji-to-collection relevance) used by the prediction scoredetermination module 606 in calculating a prediction score.

FIG. 10 is a block diagram illustrating an example software architecture1006, which may be used in conjunction with various hardwarearchitectures herein described. FIG. 10 is a non-limiting example of asoftware architecture and it will be appreciated that many otherarchitectures may be implemented to facilitate the functionalitydescribed herein. The software architecture 1006 may execute on hardwaresuch as machine 1100 of FIG. 11 that includes, among other things,processors 1104, memory/storage 1106, and I/O components 1118. Arepresentative hardware layer 1052 is illustrated and can represent, forexample, the machine 1100 of FIG. 11. The representative hardware layer1052 includes a processing unit 1054 having associated executableinstructions 1004. Executable instructions 1004 represent the executableinstructions of the software architecture 1006, including implementationof the methods, components and so forth described herein. The hardwarelayer 1052 also includes memory or storage modules memory/storage 1056,which also have executable instructions 1004. The hardware layer 1052may also comprise other hardware 1058.

In the example architecture of FIG. 10, the software architecture 1006may be conceptualized as a stack of layers where each layer providesparticular functionality. For example, the software architecture 1006may include layers such as an operating system 1002, libraries 1020,applications 1016, and a presentation layer 1014. Operationally, theapplications 1016 or other components within the layers may invokeapplication programming interface (API) calls 1008 through the softwarestack and receive a response in the example form of messages 1012 to theAPI calls 1008. The layers illustrated are representative in nature andnot all software architectures have all layers. For example, some mobileor special purpose operating systems may not provide aframeworks/middleware 1018, while others may provide such a layer. Othersoftware architectures may include additional or different layers.

The operating system 1002 may manage hardware resources and providecommon services. The operating system 1002 may include, for example, akernel 1022, services 1024 and drivers 1026. The kernel 1022 may act asan abstraction layer between the hardware and the other software layers.For example, the kernel 1022 may be responsible for memory management,processor management (e.g., scheduling), component management,networking, security settings, and so on. The services 1024 may provideother common services for the other software layers. The drivers 1026are responsible for controlling or interfacing with the underlyinghardware. For instance, the drivers 1026 include display drivers, cameradrivers, Bluetooth® drivers, flash memory drivers, serial communicationdrivers (e.g., Universal Serial Bus (USB) drivers), Wi-Fi® drivers,audio drivers, power management drivers, and so forth depending on thehardware configuration.

The libraries 1020 provide a common infrastructure that is used by theapplications 1016 or other components or layers. The libraries 1020provide functionality that allows other software components to performtasks in an easier fashion than to interface directly with theunderlying operating system 1002 functionality (e.g., kernel 1022,services 1024, or drivers 1026). The libraries 1020 may include systemlibraries 1044 (e.g., C standard library) that may provide functionssuch as memory allocation functions, string manipulation functions,mathematical functions, and the like. In addition, the libraries 1020may include API libraries 1046 such as media libraries (e.g., librariesto support presentation and manipulation of various media formats suchas MPEG4, H.264, MP3, AAC, AMR, JPG, PNG), graphics libraries (e.g., anOpenGL framework that may be used to render 2D and 3D graphic content ona display), database libraries (e.g., SQLite that may provide variousrelational database functions), web libraries (e.g., WebKit that mayprovide web browsing functionality), and the like. The libraries 1020may also include a wide variety of other libraries 1048 to provide manyother APIs to the applications 1016 and other softwarecomponents/modules.

The frameworks/middleware 1018 (also sometimes referred to asmiddleware) provide a higher-level common infrastructure that may beused by the applications 1016 or other software components/modules. Forexample, the frameworks/middleware 1018 may provide various graphic userinterface (GUI) functions, high-level resource management, high-levellocation services, and so forth. The frameworks/middleware 1018 mayprovide a broad spectrum of other APIs that may be used by theapplications 1016 or other software components/modules, some of whichmay be specific to a particular operating system 1002 or platform.

The applications 1016 include built-in applications 1038 or third-partyapplications 1040. Examples of representative built-in applications 1038may include, but are not limited to, a contacts application, a browserapplication, a book reader application, a location application, a mediaapplication, a messaging application, or a game application. Third-partyapplications 1040 may include an application developed using theANDROID™ or IOS™ software development kit (SDK) by an entity other thanthe vendor of the particular platform, and may be mobile softwarerunning on a mobile operating system such as IOS™, ANDROID™, WINDOWS®Phone, or other mobile operating systems. The third-party applications1040 may invoke the API calls 1008 provided by the mobile operatingsystem (such as operating system 1002) to facilitate functionalitydescribed herein.

The applications 1016 may use built-in operating system functions (e.g.,kernel 1022, services 1024, or drivers 1026), libraries 1020, andframeworks/middleware 1018 to create user interfaces to interact withusers of the system. Alternatively, or additionally, in some systems,interactions with a user may occur through a presentation layer, such aspresentation layer 1014. In these systems, the application/component“logic” can be separated from the aspects of the application/componentthat interact with a user.

FIG. 11 is a block diagram illustrating components of a machine 1100,according to some embodiments, able to read instructions from amachine-readable medium (e.g., a computer-readable storage medium) andperform any one or more of the methodologies discussed herein.Specifically, FIG. 11 shows a diagrammatic representation of the machine1100 in the example form of a computer system, within which instructions1110 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 1100 to perform any oneor more of the methodologies discussed herein may be executed. As such,the instructions 1110 may be used to implement modules or componentsdescribed herein. The instructions 1110 transform the general,non-programmed machine 1100 into a particular machine 1100 programmed tocarry out the described and illustrated functions in the mannerdescribed. In alternative embodiments, the machine 1100 operates as astandalone device or may be coupled (e.g., networked) to other machines.In a networked deployment, the machine 1100 may operate in the capacityof a server machine or a client machine in a server-client networkenvironment, or as a peer machine in a peer-to-peer (or distributed)network environment. The machine 1100 may comprise, but not be limitedto, a server computer, a client computer, a personal computer (PC), atablet computer, a laptop computer, a netbook, a set-top box (STB), apersonal digital assistant (PDA), an entertainment media system, acellular telephone, a smart phone, a mobile device, a wearable device(e.g., a smart watch), a smart home device (e.g., a smart appliance),other smart devices, a web appliance, a network router, a networkswitch, a network bridge, or any machine capable of executing theinstructions 1110, sequentially or otherwise, that specify actions to betaken by machine 1100. Further, while only a single machine 1100 isillustrated, the term “machine” shall also be taken to include acollection of machines that individually or jointly execute theinstructions 1110 to perform any one or more of the methodologiesdiscussed herein.

The machine 1100 may include processors 1104, memory memory/storage1106, and I/O components 1118, which may be configured to communicatewith each other such as via a bus 1102. The memory/storage 1106 mayinclude a memory 1114, such as a main memory, or other memory storage,and a storage unit 1116, both accessible to the processors 1104 such asvia the bus 1102. The storage unit 1116 and memory 1114 store theinstructions 1110 embodying any one or more of the methodologies orfunctions described herein. The instructions 1110 may also reside,completely or partially, within the memory 1114, within the storage unit1116, within at least one of the processors 1104 (e.g., within theprocessor's cache memory), or any suitable combination thereof, duringexecution thereof by the machine 1100. Accordingly, the memory 1114, thestorage unit 1116, and the memory of processors 1104 are examples ofmachine-readable media.

The I/O components 1118 may include a wide variety of components toreceive input, provide output, produce output, transmit information,exchange information, capture measurements, and so on. The specific I/Ocomponents 1118 that are included in a particular machine 1100 willdepend on the type of machine. For example, portable machines such asmobile phones will likely include a touch input device or other suchinput mechanisms, while a headless server machine will likely notinclude such a touch input device. It will be appreciated that the I/Ocomponents 1118 may include many other components that are not shown inFIG. 11. The I/O components 1118 are grouped according to functionalitymerely for simplifying the following discussion and the grouping is inno way limiting. In various embodiments, the I/O components 1118 mayinclude output components 1126 and input components 1128. The outputcomponents 1126 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), haptic components (e.g., avibratory motor, resistance mechanisms), other signal generators, and soforth. The input components 1128 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or other pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location or force of touches or touch gestures, orother tactile input components), audio input components (e.g., amicrophone), and the like.

In further embodiments, the I/O components 1118 may include biometriccomponents 1130, motion components 1134, environment components 1136, orposition components 1138 among a wide array of other components. Forexample, the biometric components 1130 may include components to detectexpressions (e.g., hand expressions, facial expressions, vocalexpressions, body gestures, or eye tracking), measure biosignals (e.g.,blood pressure, heart rate, body temperature, perspiration, or brainwaves), identify a person (e.g., voice identification, retinalidentification, facial identification, fingerprint identification, orelectroencephalogram based identification), and the like. The motioncomponents 1134 may include acceleration sensor components (e.g.,accelerometer), gravitation sensor components, rotation sensorcomponents (e.g., gyroscope), and so forth. The environment components1136 may include, for example, illumination sensor components (e.g.,photometer), temperature sensor components (e.g., one or morethermometer that detect ambient temperature), humidity sensorcomponents, pressure sensor components (e.g., barometer), acousticsensor components (e.g., one or more microphones that detect backgroundnoise), proximity sensor components (e.g., infrared sensors that detectnearby objects), gas sensors (e.g., gas detection sensors to detectionconcentrations of hazardous gases for safety or to measure pollutants inthe atmosphere), or other components that may provide indications,measurements, or signals corresponding to a surrounding physicalenvironment. The position components 1138 may include location sensorcomponents (e.g., a Global Position system (GPS) receiver component),altitude sensor components (e.g., altimeters or barometers that detectair pressure from which altitude may be derived), orientation sensorcomponents (e.g., magnetometers), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 1118 may include communication components 1140operable to couple the machine 1100 to a network 1132 or devices 1120via coupling 1122 and coupling 1124 respectively. For example, thecommunication components 1140 may include a network interface componentor other suitable device to interface with the network 1132. In furtherexamples, communication components 1140 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, Near Field Communication (NFC) components, Bluetooth®components (e.g., Bluetooth® Low Energy), Wi-Fi® components, and othercommunication components to provide communication via other modalities.The devices 1120 may be another machine or any of a wide variety ofperipheral devices (e.g., a peripheral device coupled via a UniversalSerial Bus (USB)).

Moreover, the communication components 1140 may detect identifiers orinclude components operable to detect identifiers. For example, thecommunication components 1140 may include Radio Frequency Identification(RFID) tag reader components, NFC smart tag detection components,optical reader components (e.g., an optical sensor to detectone-dimensional bar codes such as Universal Product Code (UPC) bar code,multi-dimensional bar codes such as Quick Response (QR) code, Azteccode, Data Matrix, Dataglyph, MaxiCode, PDF417, Ultra Code, UCC RSS-2Dbar code, and other optical codes), or acoustic detection components(e.g., microphones to identify tagged audio signals). In addition, avariety of information may be derived via the communication components1140, such as, location via Internet Protocol (IP) geo-location,location via Wi-Fi® signal triangulation, location via detecting a NFCbeacon signal that may indicate a particular location, and so forth.

As used herein, “ephemeral message” can refer to a message (e.g.,message item) that is accessible for a time-limited duration (e.g.,maximum of 11 seconds). An ephemeral message may comprise a textcontent, image content, audio content, video content and the like. Theaccess time for the ephemeral message may be set by the message senderor, alternatively, the access time may be a default setting or a settingspecified by the recipient. Regardless of the setting technique, anephemeral message is transitory. A message duration parameter associatedwith an ephemeral message may provide a value that determines the amountof time that the ephemeral message can be displayed or accessed by areceiving user of the ephemeral message. An ephemeral message may beaccessed or displayed using a messaging client software applicationcapable of receiving and displaying content of the ephemeral message,such as an ephemeral messaging application.

As also used herein, “ephemeral message story” can refer to a collectionof ephemeral message content items that is accessible for a time-limitedduration, similar to an ephemeral message. An ephemeral message storymay be sent from one user to another, and may be accessed or displayedusing a messaging client software application capable of receiving anddisplaying the collection of ephemeral message content items, such as anephemeral messaging application.

Throughout this specification, plural instances may implementcomponents, operations, or structures described as a single instance.Although individual operations of one or more methods are illustratedand described as separate operations, one or more of the individualoperations may be performed concurrently, and nothing requires that theoperations be performed in the order illustrated. Structures andfunctionality presented as separate components in example configurationsmay be implemented as a combined structure or component. Similarly,structures and functionality presented as a single component may beimplemented as separate components. These and other variations,modifications, additions, and improvements fall within the scope of thesubject matter herein.

Although an overview of the inventive subject matter has been describedwith reference to specific embodiments, various modifications andchanges may be made to these embodiments without departing from thebroader scope of embodiments of the present disclosure.

The embodiments illustrated herein are described in sufficient detail toenable those skilled in the art to practice the teachings disclosed.Other embodiments may be used and derived therefrom, such thatstructural and logical substitutions and changes may be made withoutdeparting from the scope of this disclosure. The detailed description,therefore, is not to be taken in a limiting sense, and the scope ofvarious embodiments is defined only by the appended claims, along withthe full range of equivalents to which such claims are entitled.

As used herein, modules may constitute software modules (e.g., codestored or otherwise embodied in a machine-readable medium or in atransmission medium), hardware modules, or any suitable combinationthereof. A “hardware module” is a tangible (e.g., non-transitory)physical component (e.g., a set of one or more processors) capable ofperforming certain operations and may be configured or arranged in acertain physical manner. In various embodiments, one or more computersystems or one or more hardware modules thereof may be configured bysoftware (e.g., an application or portion thereof) as a hardware modulethat operates to perform operations described herein for that module.

In some embodiments, a hardware module may be implementedelectronically. For example, a hardware module may include dedicatedcircuitry or logic that is permanently configured to perform certainoperations. A hardware module may be or include a special-purposeprocessor, such as a field programmable gate array (FPGA) or an ASIC. Ahardware module may also include programmable logic or circuitry that istemporarily configured by software to perform certain operations. As anexample, a hardware module may include software encompassed within a CPUor other programmable processor.

Considering embodiments in which hardware modules are temporarilyconfigured (e.g., programmed), each of the hardware modules need not beconfigured or instantiated at any one instance in time. For example,where a hardware module includes a CPU configured by software to becomea special-purpose processor, the CPU may be configured as respectivelydifferent special-purpose processors (e.g., each included in a differenthardware module) at different times. Software (e.g., a software module)may accordingly configure one or more processors, for example, to becomeor otherwise constitute a particular hardware module at one instance oftime and to become or otherwise constitute a different hardware moduleat a different instance of time.

Hardware modules can provide information to, and receive informationfrom, other hardware modules. Accordingly, described hardware modulesmay be regarded as being communicatively coupled. Where multiplehardware modules exist contemporaneously, communications may be achievedthrough signal transmission (e.g., over suitable circuits and buses)between or among two or more of the hardware modules. In embodiments inwhich multiple hardware modules are configured or instantiated atdifferent times, communications between such hardware modules may beachieved, for example, through the storage and retrieval of informationin memory structures to which the multiple hardware modules have access.For example, one hardware module may perform an operation and store theoutput of that operation in a memory (e.g., a memory device) to which itis communicatively coupled. A further hardware module may then, at alater time, access the memory to retrieve and process the stored output.Hardware modules may also initiate communications with input or outputdevices, and can operate on a resource (e.g., a collection ofinformation from a computing resource).

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Whether temporarily or permanentlyconfigured, such processors may constitute processor-implemented modulesthat operate to perform one or more operations or functions describedherein. As used herein, “processor-implemented module” refers to ahardware module in which the hardware includes one or more processors.Accordingly, the operations described herein may be at least partiallyprocessor-implemented, hardware-implemented, or both, since a processoris an example of hardware, and at least some operations within any oneor more of the methods discussed herein may be performed by one or moreprocessor-implemented modules, hardware-implemented modules, or anysuitable combination thereof.

As used herein, the term “or” may be construed in either an inclusive orexclusive sense. The terms “a” or “an” should be read as meaning “atleast one,” “one or more,” or the like. The use of words and phrasessuch as “one or more,” “at least,” “but not limited to,” or other likephrases shall not be read to mean that the narrower case is intended orrequired in instances where such broadening phrases may be absent.

Boundaries between various resources, operations, modules, engines, anddata stores are somewhat arbitrary, and particular operations areillustrated in a context of specific illustrative configurations. Otherallocations of functionality are envisioned and may fall within a scopeof various embodiments of the present disclosure. In general, structuresand functionality presented as separate resources in the exampleconfigurations may be implemented as a combined structure or resource.Similarly, structures and functionality presented as a single resourcemay be implemented as separate resources. These and other variations,modifications, additions, and improvements fall within a scope ofembodiments of the present disclosure as represented by the appendedclaims. The specification and drawings are, accordingly, to be regardedin an illustrative rather than a restrictive sense.

The description above includes systems, methods, devices, instructions,and computer media (e.g., computing machine program products) thatembody illustrative embodiments of the disclosure. In the description,for the purposes of explanation, numerous specific details are set forthin order to provide an understanding of various embodiments of theinventive subject matter. It will be evident, however, to those skilledin the art, that embodiments of the inventive subject matter may bepracticed without these specific details. In general, well-knowninstruction instances, protocols, structures, and techniques are notnecessarily shown in detail.

What is claimed is:
 1. A method comprising: generating, by one or moreprocessors, corpus data from a set of features of a collection of mediacontent items, the collection of media content items comprising at leastone item that is a digital image or a digital video, the set of featurescomprising at least one visual label, category, or geographical locationassociated with the collection of media content items, the corpus datacomprising a set of textual n-grams identified in the set of features;determining, by the one or more processors, a set of candidate graphicalelements for the collection of media content items based on the set oftextual n-grams from the corpus data, based on a set of first mappingsthat maps at least one textual n-gram in the set of textual n-grams to afirst graphical element, and based on a set of second mappings that mapsat least the first graphical element to a second graphical element, thesecond graphical element being determined to be one candidate graphicalelement in the set of candidate graphical elements; determining, by theone or more processors, a set of prediction scores corresponding to theset of candidate graphical elements based on the set of features of thecollection of media content items; determining, by the one or moreprocessors, a ranking for the set of candidate graphical elements basedon the set of prediction scores; selecting, by the one or moreprocessors, a set of predicted graphical elements, from the set ofcandidate graphical elements, based on the ranking; and providing, bythe one or more processors, the set of predicted graphical elements inassociation with the collection of media content items.
 2. The method ofclaim 1, wherein the set of candidate graphical elements includes atleast one of an emoticon or an emoji.
 3. The method of claim 1, furthercomprising: tuning, by the one or more processors, the determining theset of prediction scores based on the set of features by adjusting atleast one weight used in calculating a prediction score for at least oneof the graphical element in the set of candidate graphical elements. 4.The method of claim 1, wherein the set of features further comprises atleast one of a caption or a particular graphical element associated withthe collection of media content items.
 5. The method of claim 1, furthercomprising: generating, by the one or more processors, the set of firstmappings that maps the at least one textual n-gram in the set of textualn-grams to a first graphical element based on data that provides aUnicode standard description for the first graphical element.
 6. Themethod of claim 1, further comprising: generating, by the one or moreprocessors, the set of first mappings that maps the at least one textualn-gram in the set of textual n-grams to a first graphical element basedon co-occurrences of the first graphical element and the at least onetextual n-gram with respect to at least one other collection of mediacontent items.
 7. The method of claim 1, wherein the set of secondmappings maps two or more graphical elements together.
 8. The method ofclaim 7, further comprising: generating, by the one or more processors,the set of second mappings mapping two or more graphical elementstogether based on the set of first mappings that maps the at least onetextual n-gram in the set of textual n-grams to a first graphicalelement.
 9. The method of claim 7, wherein the determining the set ofcandidate graphical elements based on the corpus data and further basedon the set of first mappings and the set of second mappings comprisesreplacing at least a graphical element portion of a particular textualn-gram, in the set of textual n-grams, with a particular graphicalelement according to the set of second mappings to produce a modifiedtextual n-gram, the set of candidate graphical elements including themodified textual n-gram.
 10. The method of claim 1, wherein thedetermining the set of candidate graphical elements based on the corpusdata and further based on the set of first mappings comprises replacingat least a term portion of a particular textual n-gram, in the set oftextual n-grams, with a particular graphical element according to theset of first mappings to produce a modified textual n-gram, the set ofcandidate graphical elements including the modified textual n-gram. 11.The method of claim 1, wherein the corpus data further comprises a setof frequency probability scores corresponding to the set of textualn-grams.
 12. The method of claim 11, wherein the determining the set ofprediction scores corresponding to the set of candidate graphicalelements based on the set of features comprises using a termfrequency-inverse document frequency (TF-IDF) algorithm to determine theset of prediction scores based Oil the set of frequency probabilityscores from the corpus data.
 13. A system comprising: one or moreprocessors; and one or more machine-readable mediums storinginstructions that, when executed by the one or more processors, causethe system to perform operations comprising: generating corpus data froma set of features of a collection of media content items, the collectionof media content items comprising at least one item that is a digitalimage or a digital video, the set of features comprising at least onevisual label, category, or geographical location associated with thecollection of media content items, the corpus data comprising a set oftextual n-grams identified in the set of features; determining a set ofcandidate graphical elements for the collection of media content itemsbased on the set of textual n-grams from the corpus data, based on a setof first mappings that maps at least one textual n-gram in the set oftextual n-grams to a first graphical element, and based on a set ofsecond mappings that maps at least the first graphical element to asecond graphical element, the second graphical element being determinedto be one candidate graphical element in the set of candidate graphicalelements; determining a set of prediction scores corresponding to theset of candidate graphical elements based on the set of features of thecollection of media content items; determining a ranking for the set ofcandidate graphical elements based on the set of prediction scores;selecting a set of predicted graphical elements, from the set ofcandidate graphical elements, based on the ranking; and providing theset of predicted graphical elements in association with the collectionof media content items.
 14. The system of claim 13, wherein the set ofcandidate graphical elements includes at least one of an emoticon or anemoji.
 15. The system of claim 13, wherein the operations furthercomprise: tuning the determining the set of prediction scores based onthe set of features by adjusting at least one weight used in calculatinga prediction score for at least one graphical element in the set ofcandidate graphical elements.
 16. The system of claim 13, wherein theset of features further comprises at least one of a caption or aparticular graphical element associated with the collection of mediacontent items.
 17. The system of claim 13, wherein the operationsfurther comprise: generating the set of first mappings that maps the atleast one textual n-gram in the set of textual n-grams to a firstgraphical element based on data that provides a Unicode standarddescription for the first graphical element.
 18. The system of claim 13,wherein the operations further comprise: generating the set of firstmappings that maps the at least one textual n-gram in the set of textualn-grams to a first graphical element based on co-occurrences of thefirst graphical element and the at least one textual n-gram with respectto at least one other collection of media content items.
 19. The systemof claim 13, wherein the set of second mappings maps two or moregraphical elements together.
 20. A non-transitory computer-readablemedium storing instructions that, when executed by one or more computerprocessors, cause the one or more computer processors to performoperations comprising: generating corpus data from a set of features ofa collection of media content items, the collection of media contentitems comprising at least one item that is a digital image or a digitalvideo, the set of features comprising at least one visual label,category, or geographical location associated with the collection ofmedia content items, the corpus data comprising a set of textual n-gramsidentified in the set of features; determining a set of candidategraphical elements for the collection of media content items based onthe set of textual n-grams from the corpus data, based on a set of firstmappings that maps at least one textual n-gram in the set of textualn-grams to a first graphical element, and based on a set of secondmappings that maps at least the first graphical element to a secondgraphical element, the second graphical element being determined to beone candidate graphical element in the set of candidate graphicalelements; determining a set of prediction scores corresponding to theset of candidate graphical elements based on the set of features of thecollection of media content items; determining a ranking for the set ofcandidate graphical elements based on the set of prediction scores;selecting a set of predicted graphical elements, from the set ofcandidate graphical elements, based on the ranking; and providing theset of predicted graphical elements in association with the collectionof media content items.